Causal Inference for fMRI Time Series Data With Systematic Errors of Measurement in a Balanced On/Off Study of Social Evaluative Threat

Michael E. Sobel and Martin A. Lindquist

Journal of the American Statistical Association, 2014, vol. 109, issue 507, 967-976

Abstract: Functional magnetic resonance imaging (fMRI) has facilitated major advances in understanding human brain function. Neuroscientists are interested in using fMRI to study the effects of external stimuli on brain activity and causal relationships among brain regions, but have not stated what is meant by causation or defined the effects they purport to estimate. Building on Rubin's causal model, we construct a framework for causal inference using blood oxygenation level dependent (BOLD) fMRI time series data. In the usual statistical literature on causal inference, potential outcomes, assumed to be measured without systematic error, are used to define unit and average causal effects. However, in general the potential BOLD responses are measured with stimulus dependent systematic error. Thus we define unit and average causal effects that are free of systematic error. In contrast to the usual case of a randomized experiment where adjustment for intermediate outcomes leads to biased estimates of treatment effects, here the failure to adjust for task dependent systematic error leads to biased estimates. We therefore adjust for systematic error using measured "noise covariates," using a linear mixed model to estimate the effects and the systematic error. Our results are important for neuroscientists, who typically do not adjust for systematic error. They should also prove useful to researchers in other areas where responses are measured with error and in fields where large amounts of data are collected on relatively few subjects. To illustrate our approach, we reanalyze data from a social evaluative threat task, comparing the findings with results that ignore systematic error.

Date: 2014
References: Add references at CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://hdl.handle.net/10.1080/01621459.2014.922886 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlasa:v:109:y:2014:i:507:p:967-976

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UASA20

DOI: 10.1080/01621459.2014.922886

Access Statistics for this article

Journal of the American Statistical Association is currently edited by Xuming He, Jun Liu, Joseph Ibrahim and Alyson Wilson

More articles in Journal of the American Statistical Association from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().